P.060 Utility analysis of continuous video EEG (cvEEG) monitoring during the treatment of hypoxic ischemic encephalopathy (HIE) in the NICU
Bibliographic record
Abstract
Background: Therapeutic hypothermia (TH) improves the outcome in HIE but cvEEG is vital to detect any seizures that occur. Unfortunately, the costs associated with cvEEG can make it impractical. We studied outcomes in TH with the objective of optimizing the length of cvEEG required. Methods: Term infants with HIE were treated with 72 h of TH followed by 6 h of rewarming. cvEEG reports were quantified (background, sharp transients, seizures) and compared with pre and post-cooling variables to determine whether risk stratification was possible. Results: 25/78 infants had seizures during the TH, however, most seizures occurred early, with 7 infants seizing prior to cooling and 15 having their first seizure within 24h. Only 3 infants had their first seizure between 24-48h and none were recorded after. Novel seizures after 24h were brief and did not require treatment. EEG variables such as frequent sharp transients and first seizures within 24h were correlated with MRI abnormalities. Conclusions: For the majority of infants undergoing TH, 24h of cvEEG may be sufficient with few infants requiring longer than 48h. A combination of clinical variables (abnormal neurological exam) and EEG traits (frequency of discharges, seizures) can help to decide on the likelihood of seizures and length of EEG recording needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".